Find the best solution for your challenge
How to host and start a data science challenge

We provide efficient
AI solutions that help your business grow

Zindi data scientists use AI to solve business problems in finance, logistics, e-commerce, retail, agriculture, health care and many more industries.

At Zindi we build world-class AI solutions by developing and hosting data science competitions.

Your organisation gets the best solutions built by our amazing community of data scientists and engineers.

How we help
Flexible resourcing
Zindi capacity augments your existing team and be dialled up or down as your needs evolve
The best solutions
We have data scientists from across Africa and the world competing to build the best machine learning model for your problem
Full rights to code
You own the top three best-performing algorithms
Global exposure
Gain global exposure as an organisation on the cutting edge of data science
Hosting a challenge? Here are the steps
1
Identify a challenge

We help your organisation identify and define a pressing or business challenge. Prizes are set with your organisation, with complexity of the challenge as a determining factor.

2
We help you collate relevant data sets

Zindi data scientists work with your organisation to determine which datasets are needed to solve the challenge and assist in building the datasets in the appropriate format.

Data is fully anonymised and masked where needed. Challenge solutions are built on static data - models can be implemented in real-time post competition if required.

3
The challenge is posted online

A description of the challenge and prize money up for grabs, as well as the challenge datasets, are posted on zindi.africa. The competition is promoted to the Zindi community, advertised on social media platforms like LinkedIn, Twitter, and Facebook, and spread throughout our partner network. For clients for whom privacy is an overriding concern, fully private competitions are also available.

4
Data scientists compete to solve your challenge

Up to a thousand data scientists (or more!) download training and test datasets and build solutions. Typically, solutions comprise data processing, feature building and one or more machine learning techniques.

5
Solutions are ranked and cash prizes are awarded to the winning solution

Competitors can submit several solutions during the competition. Their submissions, based on the test set they dowloaded, are ranked in real-time on an automated leaderboard. At the close of the competition, submissions are scored against a private (i.e. hidden) dataset based on the accuracy they achieve. The final rankings are always 100% objective and fair, and we ensure their relevance to you and your organisation’s needs.

Zimnat reduced customer churn by 30% month to month

"We asked Zindi to give us a way to predict when a customer would 'churn' or leave as a paying customer. We built the winning models into a dashboard with a customer profile and the likelihood of them leaving the business, and that dashboard is now given to our customer care agents."

Oswin Zulu, Chief Digital Officer

Industry examples and the areas where AI can make an impact
Retail
  • Sales and volume forecasting
  • Inventory management
  • Delivery route optimisation
  • Personalised customer recommendations
  • Dynamic pricing
  • Product classification
Financial Services
  • Customer personalisation and retention
  • Compliance and process automation
  • Fraud detection
  • Credit risk management
Healthcare
  • Early diagnoses
  • Clinical decision making
  • Drug discovery
  • Automated image classification
Telecoms
  • Early detection of service degradations
  • Customer traffic prediction
  • Customer retention
  • Cross-selling/Up-selling recommendations
  • ARPU optimisation
Manufacturing
  • Component failure prediction
  • Demand prediction
  • Quality assurance
  • Inventory management
  • Raw material pricing predictions
Don't see your industry listed here? Drop us a line anyway.
We're confident we can craft a solution that uniquely meets
your needs.
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